9.6 A new paradigm for constraining aerosol climate impacts by combining multiangular and polarimetric remote sensing with chemical transport model information

Friday, 11 July 2014: 9:45 AM
Essex North (Westin Copley Place)
Olga V. Kalashnikova, JPL, Pasadena, CA; and F. Xu, C. Ge, J. Wang, M. Garay, and D. J. Diner

Aerosols have significant influences on climate and climate change – directly, because they scatter and absorb radiation, and indirectly, because they affect the reflectivity, lifetime and precipitation from clouds. Chemical Transport models (CTM) do well in predicting the types of aerosols present at a given location and time, however large uncertainties currently exist in CTM estimates of the concentration of the various aerosol species (e.g., black carbon, sulfate, dust, etc.). We present a methodology for constraining CTM aerosol fraction using multiangular spectropolarimetric data to establish the signature of specific aerosol types in top-of-atmosphere measurements. In particular, we employ the WRF-Chem model run at the University of Nebraska, and remote sensing data from the Airborne Multiangle SpectroPolarimetric Imager (AirMSPI) to explore the feasibility of this approach. AirMSPI is an 8-band UV-VIS-NIR instrument that makes highly accurate observations of the I, Q, and U Stokes parameters in 3 of the spectral bands, and flies on the NASA high-altitude ER-2 research aircraft. We select specific scenes observed by AirMSPI and use WRF-Chem to generate an initial distribution of aerosol composition. The relevant optical properties for each aerosol species are used to calculate aerosol light scattering information. This is then used in a vector (polarized) 1-D radiative transfer model to determine at-instrument Stokes parameters for the specific AirMSPI viewing geometries. As a first step, a match is sought between the CTM-predicted radiances and the AirMSPI observations. Then, the total aerosol optical depth and fractions of various aerosol species are modified via optimization to produce a better match to the observations. Finally, the results are compared to available ground-based and in situ data to validate this approach.
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